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Multimodal emotion recognition from physiological signals is receiving anincreasing amount of attention due to the impossibility to control them at willunlike behavioral reactions, thus providing more reliable information. Existingdeep learning-based methods still rely on extracted handcrafted features, nottaking full advantage of the learning ability of neural networks, and oftenadopt a single-modality approach, while human emotions are inherently expressedin a multimodal way. In this paper, we propose a hypercomplex multimodalnetwork equipped with a novel fusion module comprising parameterizedhypercomplex multiplications. Indeed, by operating in a hypercomplex domain theoperations follow algebraic rules which allow to model latent relations amonglearned feature dimensions for a more effective fusion step. We performclassification of valence and arousal from electroencephalogram (EEG) andperipheral physiological signals, employing the publicly available databaseMAHNOB-HCI surpassing a multimodal state-of-the-art network. The code of ourwork is freely available at https://github.com/ispamm/MHyEEG.

Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals